(For relevant technical literature, see the listing prior to the claims section).
Recent advances in fabrication of MEMS microelectrode arrays, together with the ability to couple the arrays directly to VLSI chips, allow simultaneous monitoring of tens and even hundreds of neurons. Moreover, clinical applications of brain-machine interfaces may require monitoring of much larger populations, even hundreds and thousands of neurons. With this large a number of recording units, communicating raw neuronal signals results in prohibitively large data rates. When sampled with 20 Ksps, eight bit precision, even a hundred electrodes would generate 16 Mbps, too large for common methods of low-power wireless communications. Evidently, some form of data reduction must be applied prior to communication.
It is possible to detect the presence of neuronal spikes and communicate only active portions of recorded signals. Assuming an electrode might “sense” two or three units which fire 20 times per second on average, and taking the firing event length to be 2 msec, a data rate reduction of only one-tenth can be achieved. Further reduction can be provided by restricting the communicated information to mere indications of spike presence.
An extra-cellular microelectrode typically senses activity from several units adjacent to its tip. Spike sorting applies classification techniques to assign spike waveforms of different shapes to different units. With on-chip spike sorting, the data bandwidth is reduced to 200 Kbps (almost down to one-hundreth) for the values above, assuming a 32 bit message generated for every spike.
In the prior art, another reason is known for on-chip sorting. In autonomous motor prosthetics, assuming that every spike coming from a certain electrode is generated by the same unit might prove insufficiently accurate for movement trajectory calculations. It is also known that implementation of existing algorithms for on chip spike sorting is feasible in terms of power dissipation.
In a signal recorded by an extra-cellular microelectrode, neuronal firing activity occupies the 100-10.000 Hz frequency band. Its amplitude is typically lower than 500 μV. The Local Field Potential (LFP) occupies the lower frequencies, below 100 Hz, with amplitudes below 5 mV. The signal-to-noise ratio of the combined signal is rather large. As the microelectrode noise and background noise of cortical activity are typically 5 μV, it may reach 60 dB.
Since the LFP must be filtered out prior to spike sorting, it is possible to block it right at the front-end by high-pass filtering below 100 Hz. It has been shown, however, that LFP carries important information. Several known front-end circuits pass the LFP band intact. They block the large input DC offsets, typical for neuronal signals, by high-pass filtering below 1 Hz. As the entire combined signal is passed, the minimal required precision of subsequent data acquisition is 10 bits, defined by the signal-to-noise ratio (SNR). The maximal gain is limited by the LFP magnitude and chip supply voltage. Since the firing activity (SPK) has ten times lower magnitude than the LFP, it can be amplified to only one tenth of the output swing.
Availability of multi-site neuronal electrodes, such as the Michigan probe or the Utah array, has enabled the development of highly integrated, multi-channel recording devices with large channel counts. These devices are of importance to various aspects of neurophysiological research.
Multi-site electrodes can potentially provide for simultaneous monitoring of hundreds and even thousands of neurons. The raw data rates that are generated by such populations are large. When sampled at 20 Ksps, with eight bit precision, a hundred electrodes would generate a raw data rate of 16 Mbps. Communicating such volumes of neuronal data over battery-powered wireless links, while maintaining reasonable battery life, is hardly possible with common methods of low-power wireless communications. Evidently, some form of data reduction must be applied. One possible way is to utilize some form of “lossy” data compression to reduce the raw waveform data capacity.
A method employing Wavelet Transform has been suggested. Alternatively, one might extract the significant features of the neuronal signal and limit the transmitted data to those features only. For example, it is possible to detect the presence of neuronal spikes and communicate only active portions of recorded signals, which may lead to an order of magnitude reduction in the required data rate.
Another order of magnitude reduction can be achieved if the neuronal spikes are sorted on the chip and only the notifications of spike events are transmitted to the host. Power feasibility of on-chip spike sorting with common sorting algorithms that are usually software-based has been verified. Adapting these algorithms for utilization in VLSI can yet lead to significant power savings, with only a minor sacrifice of accuracy. It has been suggested to measure and communicate only certain features of the incoming spikes. The spike sorting can subsequently operate on these features.
It would therefore be advantageous to provide an integrated system for multi-channel neuronal recording with spike/LFP separation, integrated A/D conversion and threshold detection.